The Stacked Data Podcast

In this episode of the Stacked Data Podcast, Harry sits down with Adam, Head of Analytics, Data & Engineering at MediaLab, to tackle one of the biggest gaps in the industry right now:Why so many AI projects never make it past experimentation — and what it actually takes to deliver real value.Adam has built a reputation as a pragmatic (and often sceptical) voice in the AI space. In this conversation, he breaks down what’s really driving the current wave of AI adoption — and why much of it is still fuelled by hype, not outcomes.They explore how to properly identify and validate high-value AI use cases before writing a single line of code, what “AI readiness” actually means beyond buzzwords, and how to think about testing, governance, and risk in production systems.A big theme throughout is the role of humans in the loop — why removing them too early creates more problems than it solves, and how the best teams design AI systems that augment, rather than replace, decision-making.Finally, Adam shares how to measure real impact and what it takes to scale beyond a single successful use case — turning AI from a side experiment into a meaningful business capability.If you’re a data leader or practitioner trying to cut through the noise and build AI that actually delivers, this episode is packed with practical frameworks and hard-earned lessons.

The Data Science Identity Crisis | Anurag Gangal (Spotify) on Data Roles, Analytics Engineering & AIWhat does a data scientist actually do anymore?In this episode of the Stacked Data Podcast, Harry sits down with Anurag Gangal from Spotify to unpack one of the biggest challenges in modern data: the growing confusion around data role titles.From data scientists and analytics engineers to product analysts, machine learning engineers, and more, the data landscape has become increasingly hard to navigate. Anurag shares the story behind his framework for understanding data roles, why he built his now-popular quadrant model, and how it can help both companies and individuals make better decisions.The "Data Scientist" identity crisis - Anurag’s SubstackThey explore why so many businesses still use the title data scientist to describe completely different jobs, how that creates problems in hiring and team design, and what it means for people trying to build careers in data. The conversation also dives into generalists vs specialists, the evolution of the modern data stack, and how AI could reshape the future of analytics, data science, and self-serve data work.Whether you’re a data leader, analytics engineer, data analyst, product analyst, machine learning engineer, or someone trying to break into data, this episode will help you better understand where the industry is heading.In this episode, we cover: Why the term data scientist has become so confusing The difference between analytics engineers, data analysts, product analysts, and ML engineers How to think about specialisation vs generalisation in data teams The real cost of poorly defined data roles How Anurag’s data role quadrant model helps bring clarity How to think about your career path in data How AI may change the future of data science, analytics engineering, and self-serve analyticsGuest: Anurag Gangal, Spotify Host: Harry Gollop Podcast: Stacked Data PodcastIf you enjoyed this episode, make sure to like, comment, and subscribe for more conversations with the people building the future of data.Our sponsor is Omni, an AI-powered BI platform that helps people use data to do their best work. Whether users prefer AI, Excel, point-and-click exploration, or SQL, Omni enables fast, trusted answers from a governed semantic model.The Stacked Data Podcast is produced by Cognify — a specialist recruitment partner for teams working across the modern data stack, machine learning & AI.If you’re looking to hire top data talent or exploring your next move in data, feel free to reach out to the Cognify team — we’re always happy to help and chat through the market.#DataScience #Spotify #AnalyticsEngineering #DataAnalytics #MachineLearning #DataCareers #ModernDataStack #AI #DataLeadership #ProductAnalytics #DataEngineer #Analytics #StackedDataPodcast

Dmitry Zolotukhin, VP of Analytics at Flo, leads a 30-person analytics team, driving a data-centric culture in product development. He shares insights on running over 1,500 experiments a year without sacrificing quality. Dmitry discusses how Flo transitioned from small tests to a robust experimentation framework. He emphasizes the importance of team education, process standardization, and balancing speed with trusted results. Dmitry also touches on future innovations like AI-driven decisions and meta-analysis to further enhance experimentation.

On this episode of the Stacked Data Podcast, Harry Gollop sits down with Hugo Lu, co-founder of Orchestra, to tackle one of the most common debates in modern data teams: should you build your own tools or buy off-the-shelf solutions? Hugo shares his experiences on both sides of the decision, practical frameworks for evaluating cost, opportunity, and long-term value, and real-world examples of when building or buying was the right call. Whether you’re a Head of Data, an engineer, or just curious about tooling strategy, this episode provides actionable insights to help your team make smarter, strategic decisions.

This week, I’m joined by Timothy Chan, Head of Data at Statsig.Tim has a fascinating background — he started his career as a scientist developing life-saving drugs before pivoting into data. He went on to become a Staff Data Scientist at Meta and now leads the data function at Statsig, one of the world’s leading experimentation platforms, recently acquired by OpenAI.In this episode, we dive into the power of experimentation and how Meta embedded it into every aspect of product development.We unpack:⚙️ What great experimentation really looks like🏗️ How to build a world-class experimentation function💬 How data teams can use experimentation to influence business decisions⚖️ The balance between speed, rigour, and impact👥 Why stakeholder collaboration is the true differentiator of high-performing data teamsTim shares brilliant insights from his time at both Meta and Statsig — including how to think about experimentation as a cultural capability, not just a technical one.If you care about driving real business impact with data, this is a must-listen.

In this conversation, Adam Cassar, Director of Analytics at Wise, shares his expertise on transforming analytics teams into strategic partners rather than just service providers. He discusses common barriers teams face and offers practical strategies to overcome them. Adam emphasizes the importance of proactive influence in decision-making and building strong relationships with stakeholders. His real-life examples highlight how analytics can enhance operational efficiency and foster a data-driven culture, making this a must-listen for anyone in data roles.

𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 – 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐮𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠Analytics Engineering has become one of the most in-demand roles for modern data teams in recent years. The role goes far beyond just building clean data pipelines and data models, but how do we actually measure that impact?In today’s episode of the Stacked Data Podcast, we're joined by Ross Helenius, Director of Analytics Engineering & AI Transformation Engineering at Mimecast, to unpack one of the most important (and overlooked) questions in data:👉 What does success look like for Analytics Engineering: beyond the technical?𝚆̲𝚎̲ ̲𝚎̲𝚡̲𝚙̲𝚕̲𝚘̲𝚛̲𝚎̲:̲✅ The true role of Analytics Engineering in modern data teams✅ Why measuring ROI is so hard and how  you can do this✅ How to define and track impact beyond pipelines, models, and dashboards✅ Practical KPIs and strategies to showcase business value✅ Pitfalls to avoid when proving the value of your data functionRoss brings deep experience from the intersection of data, engineering, and AI, and offers actionable insights for data leaders and practitioners alike.Whether you're leading a data team, building one, or looking to become a better AE this episode is packed with value.

Digestible. Defensible. Actionable.What does it really take to drive impact with data?Too often, analysts are left wondering:“Why didn’t anyone do anything with that insight?”This week on the Stacked Data Podcast, I’m joined by Sam Marks, Director of Analytics & Business Strategy at the Boston Bruins, to explore the gap between finding insights and driving action — and how to close it.Sam shares the Digestible, Defensible, Actionable framework he uses to turn analysis into outcomes, covering:✅ What makes an insight digestible (and where analysts go wrong)✅ How to make your work defensible and credible✅ What separates an interesting insight from an actionable one✅ How to overcome inaction from stakeholders✅ Real-world examples of the framework in actionThis one’s packed with tactical advice for any data professional tired of their work getting stuck in slide decks.

Building High-Value Data Teams and the Future of BI with Oliver Hughes, CEO of CountIn this episode of the Stacked Data Podcast, we're joined by Oliver Hughes, CEO of Count — a business intelligence platform reinventing how teams collaborate through a flexible, canvas-style interface.Together, we explore: The key principles that make data teams truly impactful Why operational clarity, effective problem-solving, and reducing time to value are so critical How Count's unique approach is transforming data collaboration beyond traditional dashboards The future of BI and how data teams can stay ahead in a fast-evolving landscapeWhether you're leading a data team, building one, or looking for smarter ways to drive insights, this episode is packed with valuable lessons.Tune in and discover a smarter, more collaborative future for data!

In this episode of the Stacked Data Podcast, we're joined by Zach from Advancing Analytics to dive deep into the world of modern data platforms.Zach walks us through his career in data and his current role at one of the UK's leading data consultancies. We explore what a modern data platform really is, why companies are investing in them, and what it takes to build one that’s scalable, reliable, and genuinely useful to the business.From core stages and common pitfalls to ensuring business alignment and future-proofing, Zach shares the lessons he's learned delivering platforms for a wide range of clients. We also zoom out to talk about what it’s like working in data consulting—what skills matter, what a typical day looks like, and what makes someone successful at Advancing Analytics.If you're interested in data architecture, consulting, or just want to understand what "modern" really means when it comes to data platforms—this one’s for you.